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=== Abstract ===
=== Abstract ===
|−|The studies in this cluster concern the design and organization of instructional activities that direct the learner’s attention to critical knowledge components. A general assumption of this cluster’s work is that these knowledge components can be acquired, strengthened, and refined more effectively through the analysis of learning tasks that identify the knowledge components in relation to the learners’ background knowledge. A corollary assumption is that under many circumstances, instruction is more effective when it directly instructs the knowledge components than when it is structured to allow only indirect learning. However, the cluster’s more general concern is that instruction needs to be based on considerations of the unique demands of the material to be learned and the learner’s relevant prior knowledge. |+|
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|−|Our general hypothesis is that the structure of instructional activities, including practice, affects learning. A slightly more specific hypothesis is that structures that require the learner to attend to the valid features of a complex stimulus lead to more robust learning than structures that do not. In some situations, the knowledge components are supported by the learner’s prior knowledge; in other situations, the learner’s prior knowledge provides a hindrance to learning. However, in all situations, the effective structure of learning events requires an analysis of the domain to be learned, one that reveals the unique demands of the to- be-learned material (relative to a learner’s background) and highlights the critical knowledge components. Learning events are then organized that recognize the demands of the task and draw attention to these components. |+|
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|−|At the micro-level, this hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that robust learning occurs when the learning event space is designed to include appropriate target paths, and when students are encouraged to take those paths. In most of the studies in this cluster, few paths are available to the student and these are well structured to lead to the acquisition and refinement of knowledge components. However, in some cases, the analysis of the task leads to paths that are not obvious. |+|
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=== Glossary ===
=== Glossary ===
Revision as of 11:33, 12 September 2006
Optimizing the practice schedule
This project plan extends dissertation work of Pavlik. In this initial work, a model-based algorithm was described to maximize the rate of learning for simple facts using flashcard like practice by determining the best schedule of presentation for a set of facts. The goal of this project plan is to develop this initial work to allow this tutor with optimized scheduling to handle more complex information and different types of learning in more natural settings (like LearnLabs). Specifically, this project plan describes extensions to the theory in two main areas.
1. Specification of a theory of refined encoding
a. Generalization practice (multimodal and bidirectional training)
b. Discrimination practice (detailed error remediation)
2. Specification of a theory of co-training
a. Effect of declarative memory chunk sequence during learning
b. Effect of declarative memory chunks on production rule learning
These theoretical directions are intended to enhance the optimization tutor by greatly extending its capabilities.
A secondary goal of the project is to link the optimization algorithm used in this project with the larger CTAT project. In this linkage the optimization algorithm would be integrated onto the current CTAT system as a curriculum management system that could select or generate problems according to the algorithm, but using CTAT interfaces. This integration will make it easier for people to use the optimal learning system and therefore increase its impact and usefulness.
How can analyses of task and learner’s knowledge lead to a structuring of instructional events that lead to robust learning?
Alternative structures of instructional events based on alternative analyses of task demands, relevant knowledge components, and learner background. Assessing the learner’s background is essentially part of the learning task analysis.
Measures of normal and robust learning.
Robust learning is increased by instructional activities that require the learner to attend to the relevant knowledge components of a learning task.
Attention to features of the task domain as a knowledge component is processed leads to associating those features with the knowledge component. If the features are valid, then forming or strengthening such associations facilitates retrieval during subsequent assessment or instruction, and thus leads to more robust learning.
- Using syntactic priming to increase robust learning (de Jong, Perfetti, DeKeyser)
- Basic skills training (MacWhinney)
- First language effects on second language grammar acquisition (Mitamura)
- Optimizing the practice schedule (Pavlik)
- Semantic grouping during vocabulary training (Tokowicz)
- Mental rotations during vocabulary training (Tokowicz)